Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Stages of Sleep01:22

Stages of Sleep

909
Sleep progresses through distinct stages, each characterized by specific brain wave patterns and physiological responses ranging from wakefulness to stages of non-rapid eye movement, known as non-REM, to rapid eye movement, referred to as REM. Understanding these stages helps in recognizing how sleep supports various bodily and cognitive functions.
Before sleep begins, in wakefulness, the brain exhibits primarily beta waves, which are high in frequency and low in amplitude, indicating alertness...
909

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Machine Learning Inverse Design Reveals a Double Narrow-Band Absorption Approach for Effective Colored Radiative Cooling Paints.

Nano letters·2026
Same author

Task-tailored Pre-processing: Fair Downstream Supervised Learning.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Two lysosomal genes ATP13A2 and GBA1 interact to drive neurodegeneration.

Molecular neurodegeneration·2026
Same author

Real-time concrete strength monitoring using piezoelectric sensors and deep learning.

Nature communications·2025
Same author

The impacts of rumination and affective symptoms on subjective-objective sleep discrepancy.

Journal of clinical and experimental neuropsychology·2025
Same author

Investigation of the impact of gynoid fat on steatotic and advanced liver diseases-Genomic and clinical perspectives from a large-scale population cohort.

Clinical nutrition (Edinburgh, Scotland)·2025
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Nov 10, 2025

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

12.0K

EEG-Based Sleep Staging Analysis with Functional Connectivity.

Hui Huang1,2, Jianhai Zhang1,2, Li Zhu1,2

  • 1School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China.

Sensors (Basel, Switzerland)
|April 3, 2021
PubMed
Summary
This summary is machine-generated.

This study reveals that analyzing brain functional connectivity using phase-locked value (PLV) in electroencephalography (EEG) data significantly improves sleep staging accuracy. Feature-level fusion across six frequency bands achieved over 96% accuracy for sleep stage classification.

Keywords:
brain functional connectivityelectroencephalography (EEG)frequency band fusionphase-locked value (PLV)sleep staging

More Related Videos

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
06:40

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

10.4K
A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy
08:23

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Published on: November 13, 2016

11.4K

Related Experiment Videos

Last Updated: Nov 10, 2025

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

12.0K
Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
06:40

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

10.4K
A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy
08:23

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Published on: November 13, 2016

11.4K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Sleep Medicine

Background:

  • Sleep staging is crucial for diagnosing sleep disorders and advancing sleep research.
  • Current methods often rely on limited channel data, neglecting global brain network interactions.
  • Brain functional connectivity offers insights into inter-regional communication during sleep.

Purpose of the Study:

  • To explore electroencephalography (EEG)-based brain mechanisms of sleep stages using functional connectivity.
  • To analyze brain interactions across different frequency bands during sleep.
  • To evaluate the efficacy of various fusion methods for sleep stage classification.

Main Methods:

  • Constructed functional connectivity networks using phase-locked value (PLV) from EEG data.
  • Analyzed brain interactions within different frequency bands (delta, alpha, etc.) during sleep stages.
  • Employed feature-level, decision-level, and hybrid fusion techniques for classification.

Main Results:

  • Phase-locked value (PLV) patterns differed across non-rapid eye movement (NREM) sleep stages, particularly in delta and alpha bands.
  • The alpha frequency band demonstrated superior discriminative capability for sleep stages.
  • Feature-level fusion incorporating six frequency bands achieved high classification accuracies (96.91% intra-subject, 96.14% inter-subjects).

Conclusions:

  • Functional connectivity analysis, especially in specific frequency bands, provides valuable insights into sleep stage mechanisms.
  • The alpha band is a key indicator for differentiating sleep stages.
  • Feature-level fusion of multi-band EEG connectivity data offers a robust approach for accurate sleep staging.